{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "BERT_Fine_Tuning_Sentence_Classification_DR.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNKaJz5j_ylj"
      },
      "source": [
        "# BERT Fine-Tuning Sentence Classification\n",
        "Copyright 2020 Denis Rothman. The text cells were taken out and replaced by titles of each cell withing the cell. The titles of the cells refer to the titles of the sections of the book. The descriptions of the cells have been rewritten for educational purposes.\n",
        "\n",
        "Contributer: George Mihaila\n",
        "\n",
        "[Reference Notebook by Chris McCormick and Nick Ryan](https://colab.research.google.com/drive/1pTuQhug6Dhl9XalKB0zUGf4FIdYFlpcX)\n",
        "\n",
        "[Reference Article by Chris McCormick and Nick Ryan](https://mccormickml.com/2019/07/22/BERT-fine-tuning/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEfSbAA4QHas",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8560cba-271c-451a-cc53-a088af8ce80e"
      },
      "source": [
        "#@title Activating the GPU\n",
        "# Main menu->Runtime->Change Runtime Type\n",
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0NmMdkZO8R6q"
      },
      "source": [
        "#@title Installing the Hugging Face PyTorch Interface for Bert\n",
        "# !pip install pytorch-pretrained-bert pytorch-nlp\n",
        "!pip install -q transformers"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ok002ceNB8E7"
      },
      "source": [
        "#@title Importing the modules\n",
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import BertTokenizer, BertConfig\n",
        "from transformers import AdamW, BertForSequenceClassification, get_linear_schedule_with_warmup\n",
        "from tqdm import tqdm, trange\n",
        "import pandas as pd\n",
        "import io\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "% matplotlib inline"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oYsV4H8fCpZ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "5a2b9ada-305c-4c38-f5c1-9daf64758ff9"
      },
      "source": [
        "#@title Specifying CUDA as the device for Torch\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "torch.cuda.get_device_name(0)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Tesla P4'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JpfK9OOJy1OY"
      },
      "source": [
        "@article{warstadt2018neural,\n",
        "    title={Neural Network Acceptability Judgments},\n",
        "    author={Warstadt, Alex and Singh, Amanpreet and Bowman, Samuel R},\n",
        "    journal={arXiv preprint arXiv:1805.12471},\n",
        "    year={2018}\n",
        "}\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UkeC7SG2krJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d576ac9-fb08-4715-bb7b-dd0b55983bac"
      },
      "source": [
        "#@title Loading the Dataset\n",
        "#source of dataset : https://nyu-mll.github.io/CoLA/\n",
        "df = pd.read_csv(\"in_domain_train.tsv\", delimiter='\\t', header=None, names=['sentence_source', 'label', 'label_notes', 'sentence'])\n",
        "df.shape"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8551, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AQfTaYDo42zu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "outputId": "abf38bbc-0bc2-42e8-9709-b25614e6f3d3"
      },
      "source": [
        "df.sample(10)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentence_source</th>\n",
              "      <th>label</th>\n",
              "      <th>label_notes</th>\n",
              "      <th>sentence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1171</th>\n",
              "      <td>r-67</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>john is as tall as that man .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5605</th>\n",
              "      <td>c_13</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>i expect soon to see the results .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6605</th>\n",
              "      <td>g_81</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>john hummed , and mary sang , at equal volumes .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3537</th>\n",
              "      <td>ks08</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>they can smile .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8483</th>\n",
              "      <td>ad03</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>alison ran</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4709</th>\n",
              "      <td>ks08</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>the news was dealt with carefully .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7690</th>\n",
              "      <td>sks13</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>i sent money to mary .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7515</th>\n",
              "      <td>sks13</td>\n",
              "      <td>0</td>\n",
              "      <td>*</td>\n",
              "      <td>mary wrote a letter to himself last year .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2443</th>\n",
              "      <td>l-93</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>a flowering plant is on the windowsill .</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5680</th>\n",
              "      <td>c_13</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>the canadian bought himself a barbecue .</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     sentence_source  ...                                          sentence\n",
              "1171            r-67  ...                     john is as tall as that man .\n",
              "5605            c_13  ...                i expect soon to see the results .\n",
              "6605            g_81  ...  john hummed , and mary sang , at equal volumes .\n",
              "3537            ks08  ...                                  they can smile .\n",
              "8483            ad03  ...                                        alison ran\n",
              "4709            ks08  ...               the news was dealt with carefully .\n",
              "7690           sks13  ...                            i sent money to mary .\n",
              "7515           sks13  ...        mary wrote a letter to himself last year .\n",
              "2443            l-93  ...          a flowering plant is on the windowsill .\n",
              "5680            c_13  ...          the canadian bought himself a barbecue .\n",
              "\n",
              "[10 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuE5BqICAne2"
      },
      "source": [
        "#@ Creating sentence, label lists and adding Bert tokens\n",
        "sentences = df.sentence.values\n",
        "\n",
        "# Adding CLS and SEP tokens at the beginning and end of each sentence for BERT\n",
        "sentences = [\"[CLS] \" + sentence + \" [SEP]\" for sentence in sentences]\n",
        "labels = df.label.values"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z474sSC6oe7A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59ff8f5c-64ab-46a5-b74b-6fb24ded5cb8"
      },
      "source": [
        "#@title Activating the BERT Tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\n",
        "tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]\n",
        "print (\"Tokenize the first sentence:\")\n",
        "print (tokenized_texts[0])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenize the first sentence:\n",
            "['[CLS]', 'our', 'friends', 'wo', 'n', \"'\", 't', 'buy', 'this', 'analysis', ',', 'let', 'alone', 'the', 'next', 'one', 'we', 'propose', '.', '[SEP]']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cp9BPRd1tMIo"
      },
      "source": [
        "#@title Processing the data\n",
        "# Set the maximum sequence length. The longest sequence in our training set is 47, but we'll leave room on the end anyway. \n",
        "# In the original paper, the authors used a length of 512.\n",
        "MAX_LEN = 128\n",
        "\n",
        "# Use the BERT tokenizer to convert the tokens to their index numbers in the BERT vocabulary\n",
        "input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n",
        "\n",
        "# Pad our input tokens\n",
        "input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDoC24LeEv3N"
      },
      "source": [
        "#@title Create attention masks\n",
        "attention_masks = []\n",
        "\n",
        "# Create a mask of 1s for each token followed by 0s for padding\n",
        "for seq in input_ids:\n",
        "  seq_mask = [float(i>0) for i in seq]\n",
        "  attention_masks.append(seq_mask)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFbE-UHvsb7-"
      },
      "source": [
        "#@title Splitting data into train and validation sets\n",
        "# Use train_test_split to split our data into train and validation sets for training\n",
        "\n",
        "train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(input_ids, labels, \n",
        "                                                            random_state=2018, test_size=0.1)\n",
        "train_masks, validation_masks, _, _ = train_test_split(attention_masks, input_ids,\n",
        "                                             random_state=2018, test_size=0.1)"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jw5K2A5Ko1RF"
      },
      "source": [
        "#@title Converting all the data into torch tensors\n",
        "# Torch tensors are the required datatype for our model\n",
        "\n",
        "train_inputs = torch.tensor(train_inputs)\n",
        "validation_inputs = torch.tensor(validation_inputs)\n",
        "train_labels = torch.tensor(train_labels)\n",
        "validation_labels = torch.tensor(validation_labels)\n",
        "train_masks = torch.tensor(train_masks)\n",
        "validation_masks = torch.tensor(validation_masks)"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GEgLpFVlo1Z-"
      },
      "source": [
        "#@title Selecting a Batch Size and Creating and Iterator\n",
        "# Select a batch size for training. For fine-tuning BERT on a specific task, the authors recommend a batch size of 16 or 32\n",
        "batch_size = 32\n",
        "\n",
        "# Create an iterator of our data with torch DataLoader. This helps save on memory during training because, unlike a for loop, \n",
        "# with an iterator the entire dataset does not need to be loaded into memory\n",
        "\n",
        "train_data = TensorDataset(train_inputs, train_masks, train_labels)\n",
        "train_sampler = RandomSampler(train_data)\n",
        "train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)\n",
        "\n",
        "validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)\n",
        "validation_sampler = SequentialSampler(validation_data)\n",
        "validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)\n"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzX6dkOHCv9F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7729a80b-bc93-41be-f999-063c77f5e2a4"
      },
      "source": [
        "#@title Bert Configuration\n",
        "# Initializing a BERT bert-base-uncased style configuration\n",
        "#@title Transformer Installation\n",
        "try:\n",
        "  import transformers\n",
        "except:\n",
        "  print(\"Installing transformers\")\n",
        "  !pip -qq install transformers\n",
        "  \n",
        "from transformers import BertModel, BertConfig\n",
        "configuration = BertConfig()\n",
        "\n",
        "# Initializing a model from the bert-base-uncased style configuration\n",
        "model = BertModel(configuration)\n",
        "\n",
        "# Accessing the model configuration\n",
        "configuration = model.config\n",
        "print(configuration)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BertConfig {\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0z3-ZV0k2qk8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "852674d6-31a6-40e5-eed1-5548bdb03584"
      },
      "source": [
        "#@title Loading the Hugging Face Bert Uncased Base Model \n",
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=2)\n",
        "model.cuda()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJO7qtU_SsDy"
      },
      "source": [
        "##@title Optimizer Grouped Parameters\n",
        "#This code is taken from:\n",
        "# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L102\n",
        "\n",
        "# Don't apply weight decay to any parameters whose names include these tokens.\n",
        "# (Here, the BERT doesn't have `gamma` or `beta` parameters, only `bias` terms)\n",
        "param_optimizer = list(model.named_parameters())\n",
        "no_decay = ['bias', 'LayerNorm.weight']\n",
        "# Separate the `weight` parameters from the `bias` parameters. \n",
        "# - For the `weight` parameters, this specifies a 'weight_decay_rate' of 0.01. \n",
        "# - For the `bias` parameters, the 'weight_decay_rate' is 0.0. \n",
        "optimizer_grouped_parameters = [\n",
        "    # Filter for all parameters which *don't* include 'bias', 'gamma', 'beta'.\n",
        "    {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],\n",
        "     'weight_decay_rate': 0.1},\n",
        "    \n",
        "    # Filter for parameters which *do* include those.\n",
        "    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)],\n",
        "     'weight_decay_rate': 0.0}\n",
        "]\n",
        "# Note - `optimizer_grouped_parameters` only includes the parameter values, not \n",
        "# the names."
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GLs72DuMODJO"
      },
      "source": [
        "#@title The Hyperparemeters for the Training Loop \n",
        "# optimizer = BertAdam(optimizer_grouped_parameters,\n",
        "#                      lr=2e-5,\n",
        "#                      warmup=.1)\n",
        "\n",
        "# Number of training epochs (authors recommend between 2 and 4)\n",
        "epochs = 4\n",
        "\n",
        "optimizer = AdamW(optimizer_grouped_parameters,\n",
        "                  lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
        "                  eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\n",
        "                  )\n",
        "# Total number of training steps is number of batches * number of epochs.\n",
        "# `train_dataloader` contains batched data so `len(train_dataloader)` gives \n",
        "# us the number of batches.\n",
        "total_steps = len(train_dataloader) * epochs\n",
        "\n",
        "# Create the learning rate scheduler.\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
        "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
        "                                            num_training_steps = total_steps)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9cQNvaZ9bnyy"
      },
      "source": [
        "#Creating the Accuracy Measurement Function\n",
        "# Function to calculate the accuracy of our predictions vs labels\n",
        "def flat_accuracy(preds, labels):\n",
        "    pred_flat = np.argmax(preds, axis=1).flatten()\n",
        "    labels_flat = labels.flatten()\n",
        "    return np.sum(pred_flat == labels_flat) / len(labels_flat)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6J-FYdx6nFE_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "791a450e-3e14-435f-bdd8-77f54e520e99"
      },
      "source": [
        "#@title The Training Loop\n",
        "t = [] \n",
        "\n",
        "# Store our loss and accuracy for plotting\n",
        "train_loss_set = []\n",
        "\n",
        "# trange is a tqdm wrapper around the normal python range\n",
        "for _ in trange(epochs, desc=\"Epoch\"):\n",
        "  \n",
        "  \n",
        "  # Training\n",
        "  \n",
        "  # Set our model to training mode (as opposed to evaluation mode)\n",
        "  model.train()\n",
        "  \n",
        "  # Tracking variables\n",
        "  tr_loss = 0\n",
        "  nb_tr_examples, nb_tr_steps = 0, 0\n",
        "  \n",
        "  # Train the data for one epoch\n",
        "  for step, batch in enumerate(train_dataloader):\n",
        "    # Add batch to GPU\n",
        "    batch = tuple(t.to(device) for t in batch)\n",
        "    # Unpack the inputs from our dataloader\n",
        "    b_input_ids, b_input_mask, b_labels = batch\n",
        "    # Clear out the gradients (by default they accumulate)\n",
        "    optimizer.zero_grad()\n",
        "    # Forward pass\n",
        "    outputs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)\n",
        "    loss = outputs['loss']\n",
        "    train_loss_set.append(loss.item())    \n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "    # Update parameters and take a step using the computed gradient\n",
        "    optimizer.step()\n",
        "\n",
        "    # Update the learning rate.\n",
        "    scheduler.step()\n",
        "    \n",
        "    \n",
        "    # Update tracking variables\n",
        "    tr_loss += loss.item()\n",
        "    nb_tr_examples += b_input_ids.size(0)\n",
        "    nb_tr_steps += 1\n",
        "\n",
        "  print(\"Train loss: {}\".format(tr_loss/nb_tr_steps))\n",
        "    \n",
        "    \n",
        "  # Validation\n",
        "\n",
        "  # Put model in evaluation mode to evaluate loss on the validation set\n",
        "  model.eval()\n",
        "\n",
        "  # Tracking variables \n",
        "  eval_loss, eval_accuracy = 0, 0\n",
        "  nb_eval_steps, nb_eval_examples = 0, 0\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "  for batch in validation_dataloader:\n",
        "    # Add batch to GPU\n",
        "    batch = tuple(t.to(device) for t in batch)\n",
        "    # Unpack the inputs from our dataloader\n",
        "    b_input_ids, b_input_mask, b_labels = batch\n",
        "    # Telling the model not to compute or store gradients, saving memory and speeding up validation\n",
        "    with torch.no_grad():\n",
        "      # Forward pass, calculate logit predictions\n",
        "      logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n",
        "    \n",
        "    # Move logits and labels to CPU\n",
        "    logits = logits['logits'].detach().cpu().numpy()\n",
        "    label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "    tmp_eval_accuracy = flat_accuracy(logits, label_ids)\n",
        "    \n",
        "    eval_accuracy += tmp_eval_accuracy\n",
        "    nb_eval_steps += 1\n",
        "\n",
        "  print(\"Validation Accuracy: {}\".format(eval_accuracy/nb_eval_steps))"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\rEpoch:   0%|          | 0/4 [00:00<?, ?it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train loss: 0.4848619277793837\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rEpoch:  25%|██▌       | 1/4 [03:06<09:20, 186.97s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy: 0.8182870370370371\n",
            "Train loss: 0.2855956747942446\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rEpoch:  50%|█████     | 2/4 [06:15<06:14, 187.34s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy: 0.8283179012345678\n",
            "Train loss: 0.17187516562857075\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\rEpoch:  75%|███████▌  | 3/4 [09:23<03:07, 187.60s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy: 0.82445987654321\n",
            "Train loss: 0.10979274041270566\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch: 100%|██████████| 4/4 [12:31<00:00, 187.93s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Validation Accuracy: 0.8217592592592593\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68xreA9JAmG5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "c7a845b2-9633-406b-bb6b-9d45a966c02f"
      },
      "source": [
        "#@title Training Evaluation\n",
        "plt.figure(figsize=(15,8))\n",
        "plt.title(\"Training loss\")\n",
        "plt.xlabel(\"Batch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(train_loss_set)\n",
        "plt.show()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mAN0LZBOOPVh"
      },
      "source": [
        "#@title Predicting and Evaluating Using the Hold-out Dataset \n",
        "df = pd.read_csv(\"out_of_domain_dev.tsv\", delimiter='\\t', header=None, names=['sentence_source', 'label', 'label_notes', 'sentence'])\n",
        "\n",
        "# Create sentence and label lists\n",
        "sentences = df.sentence.values\n",
        "\n",
        "# We need to add special tokens at the beginning and end of each sentence for BERT to work properly\n",
        "sentences = [\"[CLS] \" + sentence + \" [SEP]\" for sentence in sentences]\n",
        "labels = df.label.values\n",
        "\n",
        "tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]\n",
        "\n",
        "\n",
        "MAX_LEN = 128\n",
        "\n",
        "# Use the BERT tokenizer to convert the tokens to their index numbers in the BERT vocabulary\n",
        "input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]\n",
        "# Pad our input tokens\n",
        "input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype=\"long\", truncating=\"post\", padding=\"post\")\n",
        "# Create attention masks\n",
        "attention_masks = []\n",
        "\n",
        "# Create a mask of 1s for each token followed by 0s for padding\n",
        "for seq in input_ids:\n",
        "  seq_mask = [float(i>0) for i in seq]\n",
        "  attention_masks.append(seq_mask) \n",
        "\n",
        "prediction_inputs = torch.tensor(input_ids)\n",
        "prediction_masks = torch.tensor(attention_masks)\n",
        "prediction_labels = torch.tensor(labels)\n",
        "  \n",
        "batch_size = 32  \n",
        "\n",
        "\n",
        "prediction_data = TensorDataset(prediction_inputs, prediction_masks, prediction_labels)\n",
        "prediction_sampler = SequentialSampler(prediction_data)\n",
        "prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hba10sXR7Xi6"
      },
      "source": [
        "# Prediction on test set\n",
        "\n",
        "# Put model in evaluation mode\n",
        "model.eval()\n",
        "\n",
        "# Tracking variables \n",
        "predictions , true_labels = [], []\n",
        "\n",
        "# Predict \n",
        "for batch in prediction_dataloader:\n",
        "  # Add batch to GPU\n",
        "  batch = tuple(t.to(device) for t in batch)\n",
        "  # Unpack the inputs from our dataloader\n",
        "  b_input_ids, b_input_mask, b_labels = batch\n",
        "  # Telling the model not to compute or store gradients, saving memory and speeding up prediction\n",
        "  with torch.no_grad():\n",
        "    # Forward pass, calculate logit predictions\n",
        "    logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n",
        "\n",
        "  # Move logits and labels to CPU\n",
        "  logits = logits['logits'].detach().cpu().numpy()\n",
        "  label_ids = b_labels.to('cpu').numpy()\n",
        "  \n",
        "  # Store predictions and true labels\n",
        "  predictions.append(logits)\n",
        "  true_labels.append(label_ids)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cRaZQ4XC7kLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "819f0e2f-168e-49ee-8d45-36cbc8452711"
      },
      "source": [
        "#@title Evaluating Using Matthew's Correlation Coefficient\n",
        "# Import and evaluate each test batch using Matthew's correlation coefficient\n",
        "from sklearn.metrics import matthews_corrcoef\n",
        "matthews_set = []\n",
        "\n",
        "for i in range(len(true_labels)):\n",
        "  matthews = matthews_corrcoef(true_labels[i],\n",
        "                 np.argmax(predictions[i], axis=1).flatten())\n",
        "  matthews_set.append(matthews)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/_classification.py:900: RuntimeWarning: invalid value encountered in double_scalars\n",
            "  mcc = cov_ytyp / np.sqrt(cov_ytyt * cov_ypyp)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IUM0UA1qJaVB"
      },
      "source": [
        "The final score will be based on the entire test set, but let's take a look at the scores on the individual batches to get a sense of the variability in the metric between batches.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xytAr_C48wnu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07097f15-0ae7-41af-f114-e5d9002dced9"
      },
      "source": [
        "#@title Score of Individual Batches\n",
        "matthews_set"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.049286405809014416,\n",
              " -0.17407765595569785,\n",
              " 0.4732058754737091,\n",
              " 0.34151450937027694,\n",
              " 0.5945883900105632,\n",
              " 0.7410010097502685,\n",
              " 0.4472135954999579,\n",
              " 0.29277002188455997,\n",
              " 0.9165151389911681,\n",
              " 0.8246211251235321,\n",
              " 0.8459051693633014,\n",
              " 0.7419408268023742,\n",
              " 0.6979824404521128,\n",
              " 0.7141684885491869,\n",
              " 0.2773500981126145,\n",
              " 0.5056936741642399,\n",
              " 0.0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCYZa1lQ8Jn8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c604d08f-efeb-48bc-b5eb-bf3b0811bf6b"
      },
      "source": [
        "#@title Matthew's Evaluation on the Whole Dataset\n",
        "# Flatten the predictions and true values for aggregate Matthew's evaluation on the whole dataset\n",
        "flat_predictions = [item for sublist in predictions for item in sublist]\n",
        "flat_predictions = np.argmax(flat_predictions, axis=1).flatten()\n",
        "flat_true_labels = [item for sublist in true_labels for item in sublist]\n",
        "matthews_corrcoef(flat_true_labels, flat_predictions)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5453476037943634"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    }
  ]
}